On Limit Results for a Class of Singularly Perturbed Switching Diffusions

This work is devoted to the weak convergence analysis of a class of aggregated processes resulting from singularly perturbed switching diffusions with fast and slow motions. The processes consist of diffusion components and pure jump components. The states of the pure jump component are naturally divisible into a number of classes. Aggregate the states in each weakly irreducible class by a single state leading to an aggregated process. Under suitable conditions, it is shown that the aggregated process converges weakly to a switching diffusion process whose generator is an average with respect to the quasi-stationary distribution of the jump process.     

Asymptotic Expansions of Transition Densities for Hybrid Jump-diffusions

A class of hybrid jump diffusions modulated by a Markov chain is considered in this work.Themotivation stems from insurance risk models,and emerging applications in production planning and wirelesscommunications.The models are hybrid in that they involve both continuous dynamics and discrete events.Under suitable conditions,asymptotic expansions of the transition densities for the underlying processes aredeveloped.The formal expansions are validated and the error bounds obtained.     

Asymptotic Properties of Parabolic Systems for Null-Recurrent Switching Diffusions

This work is concerned with the asymptotic behavior of systems of parabolic equations arising fromnull-recurrent switching diffusions,which are diffusion processes modulated by continuous-time Markov chains.A sufficient condition for null recurrence is presented.Moreover,convergence rate of the solutions of systems ofhomogeneous parabolic equations under suitable conditions is established.Then a case study on verifying one ofthe conditions proposed is provided with the use of a two-state Markov chain.To verify the condition,boundaryvalue problems (BVPs) for parabolic systems are treated,which are not the usual two-point BVP type.Anextra condition in the interior is needed resulting in jump discontinuity of the derivative of the correspondingsolution.     

Relationship Between MP and DPP for the Stochastic Optimal Control Problem of Jump Diffusions

This paper is concerned with the stochastic optimal control problem of jump diffusions. The relationship between stochastic maximum principle and dynamic programming principle is discussed. Without involving any derivatives of the value function, relations among the adjoint processes, the generalized Hamiltonian and the value function are investigated by employing the notions of semijets evoked in defining the viscosity solutions. Stochastic verification theorem is also given to verify whether a given admissible control is optimal.     

Forward equations for reflected diffusions with jumps

In this paper we obtain the forward equations associated with the evolution of the density, if it exists, of reflected diffusions on the positive orthant with jumps which form a marked point process whose random jump measure possesses a stochastic intensity. These results generalize the so-called generalized Dynkin equations for piecewise deterministic jump processes due to Davis. We then consider the stationary case where the existence of a stochastic intensity is not needed. The techniques are based on local times and the use of random jump measures. We discuss the application of these results to problems arising in queuing and storage processes as well as stationary distributions of diffusions with delayed and jump reflections at the origin.This research was supported in part by the Quebec-France Cooperative Research Program and by the Natural Sciences and Engineering Research Council of Canada under Grant OGP 0042024.     

一维扩散过程的最优停止问题的研究

本文研究了一维扩散过程的最优停止问题,论证了W iener过程和几何布朗运动是F e ller过程,同时给出了一般扩散过程的处理方法.     

带Markov状态转换的跳扩散方程的数值解

研究了一类带Markov状态转换的跳扩散方程的数值解的问题,为讨论这类方程精确解的数值计算问题,我们给出了一种基于Euler格式的方程解的跳适应算法,并在一定的条件下,证明了基于这种新的跳适应算法所得到的方程的数值解是收敛于它的精确解,同时还给出了数值解收敛到其精确解的收敛阶数.最后,本文通过两个例子说明了这种跳适应算法的计算有效性.     

Asymptotic Properties of Solutions of Parabolic Equations Arising from Transient Diffusions

This work is concerned with asymptotic properties of a class of parabolic systems arising from singularly perturbed diffusions. The underlying system has a fast varying component and a slowly changing component. One of the distinct features is that the fast varying diffusion is transient. Under such a setup, this paper presents an asymptotic analysis of the solutions of such parabolic equations. Asymptotic expansions of functional satisfying the parabolic system are obtained. Error bounds are derived.     

Bernstein Diffusions for a Class of Linear Parabolic Partial Differential Equations

In this article we prove the existence of Bernstein processes which we associate in a natural way with a class of non-autonomous linear parabolic initial- and final-boundary value problems defined in bounded convex subsets of Euclidean space of arbitrary dimension. Under certain conditions regarding their joint endpoint distributions, we also prove that such processes become reversible Markov diffusions. Furthermore we show that those diffusions satisfy two Itô equations for some suitably constructed Wiener processes, and from that analysis derive Feynman–Kac representations for the solutions to the given equations. We then illustrate some of our results by considering the heat equation with Neumann boundary conditions both in a one-dimensional bounded interval and in a two-dimensional disk.     

Tensor Approximation of Generalized Correlated Diffusions and Functional Copula Operators

In this paper we develop a class of applied probabilistic continuous time but discretized state space decompositions of the characterization of a multivariate generalized diffusion process. This decomposition is novel and, in particular, it allows one to construct families of mimicking classes of processes for such continuous state and continuous time diffusions in the form of a discrete state space but continuous time Markov chain representation. Furthermore, we present this novel decomposition and study its discretization properties from several perspectives. This class of decomposition both brings insight into understanding locally in the state space the induced dependence structures from the generalized diffusion process as well as admitting computationally efficient representations in order to evaluate functionals of generalized multivariate diffusion processes, which is based on a simple rank one tensor approximation of the exact representation. In particular, we investigate aspects of semimartingale decompositions, approximation and the martingale representation for multidimensional correlated Markov processes. A new interpretation of the dependence among processes is given using the martingale approach. We show that it is possible to represent, in both continuous and discrete space, that a multidimensional correlated generalized diffusion is a linear combination of processes originated from the decomposition of the starting multidimensional semimartingale. This result not only reconciles with the existing theory of diffusion approximations and decompositions, but defines the general representation of infinitesimal generators for both multidimensional generalized diffusions and, as we will demonstrate, also for the specification of copula density dependence structures. This new result provides immediate representation of the approximate weak solution for correlated stochastic differential equations. Finally, we demonstrate desirable convergence results for the proposed multidimensional semimartingales decomposition approximations.     

Interactive Diffusions for Global Optimization

We present a novel approach, in which parallel annealing processes interact in a manner that expedites the identification of a globally optimal solution. A first annealing process operates at a faster time scale and has a drift function that converges to a non-zero (but relatively small) noise level. A second annealing process (operating at a slower time scale) is subject to a modified drift term in which the steepest descent direction is perturbed with the first annealing process density gradient. This additional term ensures that the second process is “repelled” from regions already explored. As a result, the first annealing process (which quickly identifies locally optimal solutions) allows the second annealing process to bypass locally optimal solutions recently identified, so that it can be made to converge to global optima at a faster rate. We show that, when compared to independent annealing processes, the proposed interactive diffusions can increase the speed of convergence at the expense of minimal additional computational overhead.     

Data Augmentation for Diffusions

The problem of formal likelihood-based (either classical or Bayesian) inference for discretely observed multidimensional diffusions is particularly challenging. In principle, this involves data augmentation of the observation data to give representations of the entire diffusion trajectory. Most currently proposed methodology splits broadly into two classes: either through the discretization of idealized approaches for the continuous-time diffusion setup or through the use of standard finite-dimensional methodologies discretization of the diffusion model. The connections between these approaches have not been well studied. This article provides a unified framework that brings together these approaches, demonstrating connections, and in some cases surprising differences. As a result, we provide, for the first time, theoretical justification for the various methods of imputing missing data. The inference problems are particularly challenging for irreducible diffusions, and our framework is correspondingly more complex in that case. Therefore, we treat the reducible and irreducible cases differently within the article. Supplementary materials for the article are available online.     

Langevin Diffusions and Metropolis-Hastings Algorithms

We consider a class of Langevin diffusions with state-dependent volatility. The volatility of the diffusion is chosen so as to make the stationary distribution of the diffusion with respect to its natural clock, a heated version of the stationary density of interest. The motivation behind this construction is the desire to construct uniformly ergodic diffusions with required stationary densities. Discrete time algorithms constructed by Hastings accept reject mechanisms are constructed from discretisations of the algorithms, and the properties of these algorithms are investigated.     

具有不同扩散率的两种群Ayala竞争模型的持续生存

讨论了具有不同扩散率的两种群Ａｙａｌａ竞争系统之边界平衡点和正平衡点的稳定性,得出了相关种群动力学行为的结论·同时对有扩散和无扩散时种群的动力学行为进行了比较,说明了扩散对种群持续生存的影响·     

Phase Space Transformations of Gaussian Diffusions

It is shown that for Gaussian diffusions, the transformation back to Brownian motion, usually accomplished via the Girsanov (or Feynman–Kac) formula and time-shift, can be obtained by a classical canonical, i.e. symplectic, transformation in phase space. The method is based on constants of motion, in this case the Wronskian. Similar transformations for general diffusions are briefly discussed.     

On the notion of weak stability and related issues of hybrid diffusion systems

This work is concerned with the weak stability of hybrid diffusion systems, which consist of a number of diffusions modulated by a jump process. First, we show that when the jump component is nearly completely decomposable into a number of ergodic jump processes, the overall system is still weakly stable (or positive recurrent). Next we show that even if the process contains transient states, the positive recurrence can still be preserved. Then, we examine the asymptotic distribution when only one ergodic group of states is involved in the jump process and the state space of the continuous state belongs to a compact set. Our attention is devoted to asymptotic distribution in this case. The distribution is obtained by utilizing a spectrum gap property of the underlying process.     

Off-diagonal upper estimates for the heat kernel of the Dirichlet forms on metric spaces

We give equivalent characterizations for off-diagonal upper bounds of the heat kernel of a regular Dirichlet form on the metric measure space, in two settings: for the upper bounds with the polynomial tail (typical for jump processes) and for the upper bounds with the exponential tail (for diffusions). Our proofs are purely analytic and do not use the associated Hunt process.     

Marginal Density Expansions for Diffusions and Stochastic Volatility II: Applications

In Part I (Comm. Pure Appl. Math., 67 (2014), no. 1, 40–82) we discussed density expansions for multidimensional diffusions (X¹,…,X^d), at fixed time T and projected to their first l coordinates in the small‐noise regime. Global conditions were found that replace the well‐known “not‐in‐cut‐locus” condition known from heat kernel asymptotics. In the present paper we discuss financial applications; these include tail and implied volatility asymptotics in some correlated stochastic volatility models. In particular, we solve a problem left open by A. Gulisashvili and E. M. Stein. © 2013 Wiley Periodicals, Inc.     

On the spectrum of Markov semigroups via sample path large deviations

The essential spectral radius of a sub-Markovian process is defined as the infimum of the spectral radiuses of all local perturbations of the process. When the family of rescaled processes satisfies sample path large deviation principle, the spectral radius and the essential spectral radius are expressed in terms of the rate function. The paper is motivated by applications to reflected diffusions and jump Markov processes describing stochastic networks for which the sample path large deviation principle has been established and the rate function has been identified while essential spectral radius has not been calculated.     

一类超一致椭圆扩散过程的极限定理

本文研究了分支特征为ψ（x,z）=γz^1+β（0<β≤1）形式的超一致椭圆扩散过程,当初始值X₀（dx）为底过程的某类不变测度时,给出了当空间维数d满足βd≤2时,超过程X_t依分布收敛于0测度,当βd>2时,X_t则依分布收敛于一个非退化的随机测度.